# splitscale-class: Class "splitscale" In flowCore: flowCore: Basic structures for flow cytometry data

## Description

The split scale transformation class defines a transformation that has a logarithmic scale at high values and a linear scale at low values. The transition points are chosen so that the slope of the transformation is continuous at the transition points.

The split scale transformation is defined by the function

f(parameter,r,maxValue,transitionChannel)= a*parameter+ b ~~~~parameter<=t

log_{10}(c*parameter)*\frac{r}{d} ~~~~parameter > t

where,

b=\frac{transitionChannel}{2}

d=\frac{2*log_{10}(e)*r}{transitionChannel} + log_{10}(maxValue)

t=10^{log_{10}t}

a= \frac{transitionChannel}{2*t}

log_{10}ct=\frac{(a*t+b)*d}{r}

c=10^{log_{10}ct}

## Objects from the Class

Objects can be created by calls to the constructor splitscale(parameters,r,maxValue,transitionChannel,transformationId)

## Slots

.Data:

Object of class "function" ~~

r:

Object of class "numeric"-a positive value indicating the range of the logarithmic part of the display

maxValue:

Object of class "numeric" -a positive value indicating the maximum value the transformation is applied to

transitionChannel:

Object of class "numeric" -non negative value that indicates where to split the linear vs. logarithmic transformation

parameters:

Object of class "transformation" - flow parameter to be transformed

transformationId:

Object of class "character"-unique ID to reference the transformation

## Extends

Class "singleParameterTransform", directly. Class "transform", by class "singleParameterTransform", distance 2. Class "transformation", by class "singleParameterTransform", distance 3. Class "characterOrTransformation", by class "singleParameterTransform", distance 4.

## Methods

No methods defined with class "splitscale" in the signature.

## Note

The transformation object can be evaluated using the eval method by passing the data frame as an argument.The transformed parameters are returned as a matrix with a single column. (See example below)

## Author(s)

Gopalakrishnan N, F.Hahne

## References

Gating-ML Candidate Recommendation for Gating Description in Flow Cytometry

 1 2 3  dat <- read.FCS(system.file("extdata","0877408774.B08",package="flowCore")) sp1<-splitscale("FSC-H",r=768,maxValue=10000,transitionChannel=256) transOut<-eval(sp1)(exprs(dat))